{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c75da30",
   "metadata": {},
   "source": [
    "# Engaging with Multimodal Models: GPT-4V in AutoGen\n",
    "\n",
    "In AutoGen, leveraging multimodal models can be done through two different methodologies:\n",
    "1. **MultimodalAgent**: Supported by GPT-4V and other LMMs, this agent is endowed with visual cognitive abilities, allowing it to engage in interactions comparable to those of other ConversableAgents.\n",
    "2. **VisionCapability**: For LLM-based agents lacking inherent visual comprehension, we introduce vision capabilities by converting images into descriptive captions.\n",
    "\n",
    "This guide will delve into each approach, providing insights into their application and integration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f51914c",
   "metadata": {},
   "source": [
    "### Before everything starts, install AutoGen with the `lmm` option\n",
    "\n",
    "Install `pyautogen`:\n",
    "```bash\n",
    "pip install \"pyautogen[lmm]>=0.2.17\"\n",
    "```\n",
    "\n",
    "For more information, please refer to the [installation guide](/docs/installation/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67d45964",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import requests\n",
    "from PIL import Image\n",
    "from termcolor import colored\n",
    "\n",
    "import autogen\n",
    "from autogen import Agent, AssistantAgent, ConversableAgent, UserProxyAgent\n",
    "from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability\n",
    "from autogen.agentchat.contrib.img_utils import get_pil_image, pil_to_data_uri\n",
    "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent\n",
    "from autogen.code_utils import content_str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e4faf59",
   "metadata": {},
   "source": [
    "<a id=\"app-1\"></a>\n",
    "## Application 1: Image Chat\n",
    "\n",
    "In this section, we present a straightforward dual-agent architecture to enable user to chat with a multimodal agent.\n",
    "\n",
    "\n",
    "First, we show this image and ask a question.\n",
    "![](https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d5580e",
   "metadata": {},
   "source": [
    "Within the user proxy agent, we can decide to activate the human input mode or not (for here, we use human_input_mode=\"NEVER\" for conciseness). This allows you to interact with LMM in a multi-round dialogue, enabling you to provide feedback as the conversation unfolds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1db6f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "config_list_4v = autogen.config_list_from_json(\n",
    "    \"OAI_CONFIG_LIST\",\n",
    "    filter_dict={\n",
    "        \"model\": [\"gpt-4-vision-preview\"],\n",
    "    },\n",
    ")\n",
    "\n",
    "\n",
    "config_list_gpt4 = autogen.config_list_from_json(\n",
    "    \"OAI_CONFIG_LIST\",\n",
    "    filter_dict={\n",
    "        \"model\": [\"gpt-4\", \"gpt-4-0314\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n",
    "    },\n",
    ")\n",
    "\n",
    "gpt4_llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 42}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c628db",
   "metadata": {},
   "source": [
    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67157629",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
      "\n",
      "What's the breed of this dog?\n",
      "<image>.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
      "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
      "\n",
      "The dog in the image appears to be a Goldendoodle, which is a crossbreed between a Golden Retriever and a Poodle. They are known for their curly, hypoallergenic coats, which can vary in color, and their friendly and affectionate nature.\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/beibinli/autogen/autogen/agentchat/conversable_agent.py:1121: UserWarning: Cannot extract summary using last_msg: 'list' object has no attribute 'replace'. Using an empty str as summary.\n",
      "  warnings.warn(f\"Cannot extract summary using last_msg: {e}. Using an empty str as summary.\", UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ChatResult(chat_id=None, chat_history=[{'content': \"What's the breed of this dog?\\n<img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\", 'role': 'assistant'}, {'content': 'The dog in the image appears to be a Goldendoodle, which is a crossbreed between a Golden Retriever and a Poodle. They are known for their curly, hypoallergenic coats, which can vary in color, and their friendly and affectionate nature.', 'role': 'user'}], summary='', cost=({'total_cost': 0.013030000000000002, 'gpt-4-1106-vision-preview': {'cost': 0.013030000000000002, 'prompt_tokens': 1132, 'completion_tokens': 57, 'total_tokens': 1189}}, {'total_cost': 0}), human_input=[])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_agent = MultimodalConversableAgent(\n",
    "    name=\"image-explainer\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    llm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300},\n",
    ")\n",
    "\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"User_proxy\",\n",
    "    system_message=\"A human admin.\",\n",
    "    human_input_mode=\"NEVER\",  # Try between ALWAYS or NEVER\n",
    "    max_consecutive_auto_reply=0,\n",
    "    code_execution_config={\n",
    "        \"use_docker\": False\n",
    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
    ")\n",
    "\n",
    "# Ask the question with an image\n",
    "user_proxy.initiate_chat(\n",
    "    image_agent,\n",
    "    message=\"\"\"What's the breed of this dog?\n",
    "<img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\"\"\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f60521d",
   "metadata": {},
   "source": [
    "Now, input another image, and ask a followup question.\n",
    "\n",
    "![](https://th.bing.com/th/id/OIP.29Mi2kJmcHHyQVGe_0NG7QHaEo?pid=ImgDet&rs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73a2b234",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
      "\n",
      "What is this breed?\n",
      "<image>\n",
      "\n",
      "Among the breeds, which one barks less?\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
      "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
      "\n",
      "The dog in the image is a Siberian Husky. They are known for their striking appearance, with thick fur, erect triangular ears, and distinctive markings.\n",
      "\n",
      "When comparing the barking tendencies of Siberian Huskies and Goldendoodles, Huskies are generally known to bark less. Siberian Huskies are more prone to howling and vocalizing in other ways rather than barking. Goldendoodles can vary in their tendency to bark based on the traits they inherit from their Golden Retriever and Poodle parents. However, every dog is an individual, and their environment, training, and socialization can greatly influence their tendency to bark.\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Ask the question with an image\n",
    "user_proxy.send(\n",
    "    message=\"\"\"What is this breed?\n",
    "<img https://th.bing.com/th/id/OIP.29Mi2kJmcHHyQVGe_0NG7QHaEo?pid=ImgDet&rs=1>\n",
    "\n",
    "Among the breeds, which one barks less?\"\"\",\n",
    "    recipient=image_agent,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c40d0eb",
   "metadata": {},
   "source": [
    "<a id=\"app-2\"></a>\n",
    "## Application 2: Figure Creator\n",
    "\n",
    "Here, we define a `FigureCreator` agent, which contains three child agents: commander, coder, and critics.\n",
    "\n",
    "- Commander: interacts with users, runs code, and coordinates the flow between the coder and critics.\n",
    "- Coder: writes code for visualization.\n",
    "- Critics: LMM-based agent that provides comments and feedback on the generated image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e057758",
   "metadata": {},
   "outputs": [],
   "source": [
    "working_dir = \"tmp/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8eca993",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FigureCreator(ConversableAgent):\n",
    "    def __init__(self, n_iters=2, **kwargs):\n",
    "        \"\"\"\n",
    "        Initializes a FigureCreator instance.\n",
    "\n",
    "        This agent facilitates the creation of visualizations through a collaborative effort among its child agents: commander, coder, and critics.\n",
    "\n",
    "        Parameters:\n",
    "            - n_iters (int, optional): The number of \"improvement\" iterations to run. Defaults to 2.\n",
    "            - **kwargs: keyword arguments for the parent AssistantAgent.\n",
    "        \"\"\"\n",
    "        super().__init__(**kwargs)\n",
    "        self.register_reply([Agent, None], reply_func=FigureCreator._reply_user, position=0)\n",
    "        self._n_iters = n_iters\n",
    "\n",
    "    def _reply_user(self, messages=None, sender=None, config=None):\n",
    "        if all((messages is None, sender is None)):\n",
    "            error_msg = f\"Either {messages=} or {sender=} must be provided.\"\n",
    "            logger.error(error_msg)  # noqa: F821\n",
    "            raise AssertionError(error_msg)\n",
    "        if messages is None:\n",
    "            messages = self._oai_messages[sender]\n",
    "\n",
    "        user_question = messages[-1][\"content\"]\n",
    "\n",
    "        ### Define the agents\n",
    "        commander = AssistantAgent(\n",
    "            name=\"Commander\",\n",
    "            human_input_mode=\"NEVER\",\n",
    "            max_consecutive_auto_reply=10,\n",
    "            system_message=\"Help me run the code, and tell other agents it is in the <img result.jpg> file location.\",\n",
    "            is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
    "            code_execution_config={\"last_n_messages\": 3, \"work_dir\": working_dir, \"use_docker\": False},\n",
    "            llm_config=self.llm_config,\n",
    "        )\n",
    "\n",
    "        critics = MultimodalConversableAgent(\n",
    "            name=\"Critics\",\n",
    "            system_message=\"\"\"Criticize the input figure. How to replot the figure so it will be better? Find bugs and issues for the figure.\n",
    "            Pay attention to the color, format, and presentation. Keep in mind of the reader-friendliness.\n",
    "            If you think the figures is good enough, then simply say NO_ISSUES\"\"\",\n",
    "            llm_config={\"config_list\": config_list_4v, \"max_tokens\": 300},\n",
    "            human_input_mode=\"NEVER\",\n",
    "            max_consecutive_auto_reply=1,\n",
    "            #     use_docker=False,\n",
    "        )\n",
    "\n",
    "        coder = AssistantAgent(\n",
    "            name=\"Coder\",\n",
    "            llm_config=self.llm_config,\n",
    "        )\n",
    "\n",
    "        coder.update_system_message(\n",
    "            coder.system_message\n",
    "            + \"ALWAYS save the figure in `result.jpg` file. Tell other agents it is in the <img result.jpg> file location.\"\n",
    "        )\n",
    "\n",
    "        # Data flow begins\n",
    "        commander.initiate_chat(coder, message=user_question)\n",
    "        img = Image.open(os.path.join(working_dir, \"result.jpg\"))\n",
    "        plt.imshow(img)\n",
    "        plt.axis(\"off\")  # Hide the axes\n",
    "        plt.show()\n",
    "\n",
    "        for i in range(self._n_iters):\n",
    "            commander.send(\n",
    "                message=f\"Improve <img {os.path.join(working_dir, 'result.jpg')}>\",\n",
    "                recipient=critics,\n",
    "                request_reply=True,\n",
    "            )\n",
    "\n",
    "            feedback = commander._oai_messages[critics][-1][\"content\"]\n",
    "            if feedback.find(\"NO_ISSUES\") >= 0:\n",
    "                break\n",
    "            commander.send(\n",
    "                message=\"Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\\n\"\n",
    "                + feedback,\n",
    "                recipient=coder,\n",
    "                request_reply=True,\n",
    "            )\n",
    "            img = Image.open(os.path.join(working_dir, \"result.jpg\"))\n",
    "            plt.imshow(img)\n",
    "            plt.axis(\"off\")  # Hide the axes\n",
    "            plt.show()\n",
    "\n",
    "        return True, os.path.join(working_dir, \"result.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "977b9017",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mUser\u001b[0m (to Figure Creator~):\n",
      "\n",
      "\n",
      "Plot a figure by using the data from:\n",
      "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
      "\n",
      "I want to show both temperature high and low.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCommander\u001b[0m (to Coder):\n",
      "\n",
      "\n",
      "Plot a figure by using the data from:\n",
      "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
      "\n",
      "I want to show both temperature high and low.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCoder\u001b[0m (to Commander):\n",
      "\n",
      "Sure, let's first download the CSV data from the provided URL using Python and then we'll plot the figure with both temperature high and low.\n",
      "\n",
      "To start with, I'll provide you with a Python script that will download the CSV file and then plot the temperatures highs and lows.\n",
      "\n",
      "Please run this Python code:\n",
      "\n",
      "```python\n",
      "# filename: plot_temperatures.py\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# URL to download the CSV data\n",
      "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
      "\n",
      "# Download the data\n",
      "data = pd.read_csv(url)\n",
      "\n",
      "# Now, let us plot the high and low temperatures\n",
      "plt.figure(figsize=(10, 5))\n",
      "plt.plot(data['date'], data['temp_max'], label='High Temp')\n",
      "plt.plot(data['date'], data['temp_min'], label='Low Temp')\n",
      "\n",
      "# Providing labels and title\n",
      "plt.xlabel('Date')\n",
      "plt.ylabel('Temperature (°C)')\n",
      "plt.title('High and Low Temperatures in Seattle')\n",
      "plt.legend()\n",
      "\n",
      "# Rotate the dates on x-axis for better readability\n",
      "plt.xticks(rotation=45)\n",
      "plt.tight_layout()\n",
      "\n",
      "# Save the figure\n",
      "plt.savefig('result.jpg')\n",
      "\n",
      "print(\"The plot has been saved as 'result.jpg'.\")\n",
      "```\n",
      "\n",
      "Make sure you have the required packages (`pandas` and `matplotlib`) installed. If they are not installed, you can install them using `pip`:\n",
      "\n",
      "```sh\n",
      "pip install pandas matplotlib\n",
      "```\n",
      "\n",
      "After running the script, you should see a message indicating that the plot has been saved as 'result.jpg'.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is sh)...\u001b[0m\n",
      "\u001b[33mCommander\u001b[0m (to Coder):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "The plot has been saved as 'result.jpg'.\n",
      "\n",
      "Requirement already satisfied: pandas in /home/beibinli/anaconda3/lib/python3.9/site-packages (1.5.3)\n",
      "Requirement already satisfied: matplotlib in /home/beibinli/anaconda3/lib/python3.9/site-packages (3.7.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from pandas) (2022.7.1)\n",
      "Requirement already satisfied: numpy>=1.20.3 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from pandas) (1.23.5)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (23.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (10.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (5.13.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.11.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCoder\u001b[0m (to Commander):\n",
      "\n",
      "The Python script executed successfully, and the plot has been saved as 'result.jpg'. You should now be able to find the figure file at the mentioned location. \n",
      "\n",
      "The necessary packages were already installed in your environment, as indicated by your output, so no further action was required there.\n",
      "\n",
      "The figure shows the high and low temperatures for Seattle as indicated by the data in the CSV file you provided. If you have any other requests or need further assistance, feel free to ask. If not, and if you are satisfied with the result, we can conclude this task.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mCommander\u001b[0m (to Critics):\n",
      "\n",
      "Improve <image>\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCritics\u001b[0m (to Commander):\n",
      "\n",
      "The plot depicting high and low temperatures in Seattle can be improved in several ways:\n",
      "\n",
      "1. Date Axis: The dates on the x-axis are not visible or readable, which makes it hard to associate the temperature data with specific time periods. Consider formatting the date axis so that the dates are displayed at a readable angle or with sufficient spacing.\n",
      "\n",
      "2. X-axis Labeling: There appears to be an issue with the x-axis labels or ticks as it shows a dense black area, indicating overlapping or condensed labels. The frequency and format of the labels should be adjusted to prevent overlap and allow for clear reading.\n",
      "\n",
      "3. Y-axis Range: Consider starting the y-axis at a lower value if the temperature data permits, to give more space to the temperature lines and improve readability.\n",
      "\n",
      "4. Gridlines: Adding horizontal gridlines can aid in reading specific temperatures at a glance.\n",
      "\n",
      "5. Legend: The legend clearly indicates which color corresponds to high and low temperatures, which is good. Ensure the legend does not obscure any important data.\n",
      "\n",
      "6. Resolution: The actual resolution of the image may be fine, but the x-axis issue might give the illusion of a low-resolution image. Once the x-axis is corrected, the overall clarity of the chart may naturally improve.\n",
      "\n",
      "7. Color Contrast: The blue and orange color choices are generally good for contrast, but ensure they are differentiable to all users, including those who may be colorblind. Consider using colorblind-friendly palettes.\n",
      "\n",
      "8. Title: The title \"High\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCommander\u001b[0m (to Coder):\n",
      "\n",
      "Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\n",
      "The plot depicting high and low temperatures in Seattle can be improved in several ways:\n",
      "\n",
      "1. Date Axis: The dates on the x-axis are not visible or readable, which makes it hard to associate the temperature data with specific time periods. Consider formatting the date axis so that the dates are displayed at a readable angle or with sufficient spacing.\n",
      "\n",
      "2. X-axis Labeling: There appears to be an issue with the x-axis labels or ticks as it shows a dense black area, indicating overlapping or condensed labels. The frequency and format of the labels should be adjusted to prevent overlap and allow for clear reading.\n",
      "\n",
      "3. Y-axis Range: Consider starting the y-axis at a lower value if the temperature data permits, to give more space to the temperature lines and improve readability.\n",
      "\n",
      "4. Gridlines: Adding horizontal gridlines can aid in reading specific temperatures at a glance.\n",
      "\n",
      "5. Legend: The legend clearly indicates which color corresponds to high and low temperatures, which is good. Ensure the legend does not obscure any important data.\n",
      "\n",
      "6. Resolution: The actual resolution of the image may be fine, but the x-axis issue might give the illusion of a low-resolution image. Once the x-axis is corrected, the overall clarity of the chart may naturally improve.\n",
      "\n",
      "7. Color Contrast: The blue and orange color choices are generally good for contrast, but ensure they are differentiable to all users, including those who may be colorblind. Consider using colorblind-friendly palettes.\n",
      "\n",
      "8. Title: The title \"High\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCoder\u001b[0m (to Commander):\n",
      "\n",
      "To address the feedback received on the figure, we'll make the following improvements to the plot:\n",
      "\n",
      "1. Format the date axis for better readability.\n",
      "2. Adjust the frequency and format of the x-axis labels to prevent overlap.\n",
      "3. Extend the y-axis range if possible.\n",
      "4. Add horizontal gridlines for ease of reading temperatures.\n",
      "5. Ensure the legend is placed properly to avoid obscuring data.\n",
      "6. Check for clarity once x-axis labels are resolved.\n",
      "7. Use a colorblind-friendly palette for the plot.\n",
      "8. Ensure the title is descriptive and relevant.\n",
      "\n",
      "Here's an updated Python script to make these improvements:\n",
      "\n",
      "```python\n",
      "# filename: improved_plot_temperatures.py\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.dates as mdates\n",
      "\n",
      "# URL to download the CSV data\n",
      "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
      "\n",
      "# Download the data\n",
      "data = pd.read_csv(url)\n",
      "data['date'] = pd.to_datetime(data['date'])\n",
      "\n",
      "# Now, let us plot the high and low temperatures\n",
      "plt.figure(figsize=(10, 5))\n",
      "plt.plot(data['date'], data['temp_max'], label='High Temp', color='tab:blue')\n",
      "plt.plot(data['date'], data['temp_min'], label='Low Temp', color='tab:orange')\n",
      "\n",
      "# Set the x-axis major locator and formatter for better date display\n",
      "plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())\n",
      "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
      "\n",
      "# Rotate the dates on x-axis for better readability\n",
      "plt.gcf().autofmt_xdate()\n",
      "\n",
      "# Improving Y-axis Range\n",
      "temp_min = data['temp_min'].min() - 5  # Give some padding below the min temp\n",
      "temp_max = data['temp_max'].max() + 5  # Give some padding above the max temp\n",
      "plt.ylim(temp_min, temp_max)\n",
      "\n",
      "# Adding gridlines and formatting the grid\n",
      "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
      "\n",
      "# Providing labels and title\n",
      "plt.xlabel('Date')\n",
      "plt.ylabel('Temperature (°C)')\n",
      "plt.title('High and Low Temperatures in Seattle Over Time')\n",
      "\n",
      "# Adjusting legend position\n",
      "plt.legend(loc='upper right')\n",
      "\n",
      "# Check done, no need to adjust color contrast as colorblind-friendly colors are used\n",
      "\n",
      "# Save the figure in high resolution\n",
      "plt.savefig('result.jpg', dpi=300)\n",
      "\n",
      "print(\"The improved plot has been saved as 'result.jpg'.\")\n",
      "```\n",
      "\n",
      "Please execute this script. Make sure that the `matplotlib.dates` module is available in your environment before running the script. If you encounter any issues, let me know.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
      "\u001b[33mCommander\u001b[0m (to Coder):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "The improved plot has been saved as 'result.jpg'.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCoder\u001b[0m (to Commander):\n",
      "\n",
      "The improved plot has been saved successfully as 'result.jpg'. The improvements should address the feedback points provided earlier, resulting in a clearer and more readable figure.\n",
      "\n",
      "The dates on the x-axis are now formatted for better visibility, the frequency of x-axis labels has been adjusted, the y-axis range has been extended, gridlines have been added, and the legend has been positioned properly. The colors used should be distinguishable by individuals with color vision deficiencies.\n",
      "\n",
      "If no further improvements are needed, the task is now complete.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mCommander\u001b[0m (to Critics):\n",
      "\n",
      "Improve <image>\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCommander\u001b[0m (to Coder):\n",
      "\n",
      "Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\n",
      "Improve <img tmp/result.jpg>\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCoder\u001b[0m (to Commander):\n",
      "\n",
      "I apologize for any confusion, but as an AI, I don't have the capability to directly view the images generated. However, I can certainly help you further refine the plot based on standard practices if there is anything specific that you feel needs further adjustment.\n",
      "\n",
      "You mentioned feedback for a figure referenced as \"<img tmp/result.jpg>\". Since I can't view images, I'm assuming this feedback is a placeholder. In case there are specific changes or additional improvements you want to implement in the figure, please provide me with those details explicitly.\n",
      "\n",
      "For instance:\n",
      "- Are there any specific elements in the legend that still need repositioning?\n",
      "- Should the date formatting be adjusted further for better clarity?\n",
      "- Are there any other specific elements (e.g. colors, lines, labels, title) that require modification?\n",
      "\n",
      "Without specific feedback, I can suggest some general improvements, such as:\n",
      "\n",
      "- Ensuring the plot does not appear cluttered.\n",
      "- Checking if the aspect ratio is appropriate for the data being presented.\n",
      "- Verifying that the font sizes are readable.\n",
      "\n",
      "If specific feedback can be provided, we can proceed accordingly. Otherwise, please let me know if we should conclude the task.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mFigure Creator~\u001b[0m (to User):\n",
      "\n",
      "tmp/result.jpg\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ChatResult(chat_id=None, chat_history=[{'content': '\\nPlot a figure by using the data from:\\nhttps://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\\n\\nI want to show both temperature high and low.\\n', 'role': 'assistant'}, {'content': 'tmp/result.jpg', 'role': 'user'}], summary='tmp/result.jpg', cost=({'total_cost': 0}, {'total_cost': 0}), human_input=[])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creator = FigureCreator(name=\"Figure Creator~\", llm_config=gpt4_llm_config)\n",
    "\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"User\", human_input_mode=\"NEVER\", max_consecutive_auto_reply=0, code_execution_config={\"use_docker\": False}\n",
    ")\n",
    "\n",
    "user_proxy.initiate_chat(\n",
    "    creator,\n",
    "    message=\"\"\"\n",
    "Plot a figure by using the data from:\n",
    "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
    "\n",
    "I want to show both temperature high and low.\n",
    "\"\"\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a95d87c2",
   "metadata": {},
   "source": [
    "## Vision Capability: Group Chat Example with Multimodal Agent\n",
    "\n",
    "We recommend using VisionCapability for group chat managers so that it can organize and understand images better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56bd5742",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n",
      "\n",
      "Write a poet for my image:\n",
      "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
      "\u001b[33mimage-explainer-1\u001b[0m (to chat_manager):\n",
      "\n",
      "In a cozy nook by the door, where whispers of adventure pour,\n",
      "Lies a curly apricot dream, with eyes that softly gleam.\n",
      "Westphal, a name upon his tag, a dapper bow, no room to brag.\n",
      "\n",
      "A poodle or a mix so sweet, with paws that barely seem to meet\n",
      "The floor that holds his tender frame, in this quiet household game.\n",
      "A sentinel of love and joy, more charming than any toy.\n",
      "\n",
      "His gaze, a tale of gentle days, and love that in his heart blazes.\n",
      "With boots behind, like silent guards, he watches without regards\n",
      "To the world's rush and its tides, in his serene abode, he abides.\n",
      "\n",
      "Oh, little guardian of the hearth, your worth is not defined by birth.\n",
      "But by the joy you bring to life, a respite from our daily strife.\n",
      "In you, we find a friend so true, Westphal, a treasure through and through.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
      "\u001b[33mimage-explainer-2\u001b[0m (to chat_manager):\n",
      "\n",
      "A pup so young with fur of gold,\n",
      "In a house where stories unfold.\n",
      "Westphal, with your bow so neat,\n",
      "Your puppy eyes, oh so sweet.\n",
      "\n",
      "Curled up on the floor so light,\n",
      "Your coat a soft, delightful sight.\n",
      "A mix, perhaps, of breeds so kind,\n",
      "In your face, pure joy we find.\n",
      "\n",
      "With a tag that jingles slight,\n",
      "And a bow tie oh so bright,\n",
      "You lie there with such grace,\n",
      "A calm presence in this place.\n",
      "\n",
      "Behind you, boots await the rain,\n",
      "But inside, you contently remain.\n",
      "A symbol of love, a furry delight,\n",
      "A beacon of warmth, shining so bright.\n",
      "\n",
      "In a world that's fast and vast,\n",
      "Your gentle spirit holds us fast.\n",
      "Westphal, with your charm so grand,\n",
      "You bring peace to this human land.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
      "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
      "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "agent1 = MultimodalConversableAgent(\n",
    "    name=\"image-explainer-1\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    llm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300},\n",
    "    system_message=\"Your image description is poetic and engaging.\",\n",
    ")\n",
    "agent2 = MultimodalConversableAgent(\n",
    "    name=\"image-explainer-2\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    llm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300},\n",
    "    system_message=\"Your image description is factual and to the point.\",\n",
    ")\n",
    "\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"User_proxy\",\n",
    "    system_message=\"Desribe image for me.\",\n",
    "    human_input_mode=\"TERMINATE\",  # Try between ALWAYS, NEVER, and TERMINATE\n",
    "    max_consecutive_auto_reply=10,\n",
    "    code_execution_config={\n",
    "        \"use_docker\": False\n",
    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
    ")\n",
    "\n",
    "# We set max_round to 5\n",
    "groupchat = autogen.GroupChat(agents=[agent1, agent2, user_proxy], messages=[], max_round=5)\n",
    "\n",
    "vision_capability = VisionCapability(lmm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300})\n",
    "group_chat_manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=gpt4_llm_config)\n",
    "vision_capability.add_to_agent(group_chat_manager)\n",
    "\n",
    "rst = user_proxy.initiate_chat(\n",
    "    group_chat_manager,\n",
    "    message=\"\"\"Write a poet for my image:\n",
    "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\"\"\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d69b6c81",
   "metadata": {},
   "source": [
    "## Behavior with and without VisionCapability for Agents\n",
    "\n",
    "\n",
    "Here, we show the behavior of an agent with and without VisionCapability. We use the same image and question as in the previous example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aae1167f",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_no_vision = AssistantAgent(name=\"Regular LLM Agent\", llm_config=gpt4_llm_config)\n",
    "\n",
    "agent_with_vision = AssistantAgent(name=\"Regular LLM Agent with Vision Capability\", llm_config=gpt4_llm_config)\n",
    "vision_capability = VisionCapability(lmm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300})\n",
    "vision_capability.add_to_agent(agent_with_vision)\n",
    "\n",
    "\n",
    "user = UserProxyAgent(\n",
    "    name=\"User\",\n",
    "    human_input_mode=\"NEVER\",\n",
    "    max_consecutive_auto_reply=0,\n",
    "    code_execution_config={\"use_docker\": False},\n",
    ")\n",
    "\n",
    "message = \"\"\"Write a poet for my image:\n",
    "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9e81b7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mUser\u001b[0m (to Regular LLM Agent):\n",
      "\n",
      "Write a poet for my image:\n",
      "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mRegular LLM Agent\u001b[0m (to User):\n",
      "\n",
      "As an AI, I can't directly view images or web content. However, I can help you generate a poem by gathering information about the image. Please describe the image for me, including details such as the setting, prominent colors, the mood it evokes, and any specific elements like animals, nature, cityscapes, or people that you want to be highlighted in the poem. Once you provide a description of the image, I can compose a poem based on that description.\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "user.send(message=message, recipient=agent_no_vision, request_reply=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8720820f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mUser\u001b[0m (to Regular LLM Agent with Vision Capability):\n",
      "\n",
      "Write a poet for my image:\n",
      "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mRegular LLM Agent with Vision Capability\u001b[0m (to User):\n",
      "\n",
      "In apricot hues, a curl-coated pup reclines,\n",
      "With Westphal's name upon his tag that shines.\n",
      "A bow tie blooms in a brilliant blue sky,\n",
      "Amid a light-drenched floor where soft paws lie.\n",
      "\n",
      "Possessor of gazes, tender and deep,\n",
      "In the quiet foyer, his watch he keeps.\n",
      "Beneath black rubber guards of rainy days,\n",
      "He stirs a comfort, a homely embrace.\n",
      "\n",
      "His lineage drawn from the poodles' grace,\n",
      "Or maybe a mix, with a doodle's face,\n",
      "Gold or Lab, his curls are just as sweet,\n",
      "In each bouncing step, in each heartbeat.\n",
      "\n",
      "A picture of love, in a tiny frame,\n",
      "A heartbeat wrapped in an apricot mane.\n",
      "The pup in his calm, an invite to cheer,\n",
      "A whisper of joy in a pet-lover's ear.\n",
      "\n",
      "Around him, life's simple clutter does unfold,\n",
      "Yet untouched by worry, untouched by cold.\n",
      "With every breath, he claims this slice of earth,\n",
      "A master of mirth, from the moment of his birth.\n",
      "\n",
      "Paws outstretched on the soft, forgiving ground,\n",
      "His soulful eyes speak, without a sound.\n",
      "For in the sweet stillness of his gentle rest,\n",
      "Lies the simple truth that we are blessed.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "user.send(message=message, recipient=agent_with_vision, request_reply=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4d3753",
   "metadata": {},
   "source": [
    "## Custom Caption Function for Vision Capability\n",
    "\n",
    "In many use cases, we can use a custom function within the Vision Capability to transcribe an image into a caption.\n",
    "\n",
    "For instance, we can use rule-based algorithm or other models to detect the color, box, and other components inside the image.\n",
    "\n",
    "The custom model should take a path to the image and return a string caption.\n",
    "\n",
    "In the example below, the Vision Capability will call LMM to get caption and also call the custom function to get more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bff55c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_description(image_url: str, image_data: Image = None, lmm_client: object = None) -> str:\n",
    "    \"\"\"\n",
    "    This function takes an image URL and returns the description.\n",
    "\n",
    "    Parameters:\n",
    "        - image_url (str): The URL of the image.\n",
    "        - image_data (PIL.Image): The image data.\n",
    "        - lmm_client (object): The LLM client object.\n",
    "\n",
    "    Returns:\n",
    "        - str: A description of the color of the image.\n",
    "    \"\"\"\n",
    "    # Print the arguments for illustration purpose\n",
    "    print(\"image_url\", image_url)\n",
    "    print(\"image_data\", image_data)\n",
    "    print(\"lmm_client\", lmm_client)\n",
    "\n",
    "    img_uri = pil_to_data_uri(image_data)  # cast data into URI (str) format for API call\n",
    "    lmm_out = lmm_client.create(\n",
    "        context=None,\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\"type\": \"text\", \"text\": \"Describe this image in 10 words.\"},\n",
    "                    {\n",
    "                        \"type\": \"image_url\",\n",
    "                        \"image_url\": {\n",
    "                            \"url\": img_uri,\n",
    "                        },\n",
    "                    },\n",
    "                ],\n",
    "            }\n",
    "        ],\n",
    "    )\n",
    "    description = lmm_out.choices[0].message.content\n",
    "    description = content_str(description)\n",
    "\n",
    "    # Convert the image into an array of pixels.\n",
    "    pixels = np.array(image_data)\n",
    "\n",
    "    # Calculate the average color.\n",
    "    avg_color_per_row = np.mean(pixels, axis=0)\n",
    "    avg_color = np.mean(avg_color_per_row, axis=0)\n",
    "    avg_color = avg_color.astype(int)  # Convert to integer for color values\n",
    "\n",
    "    # Format the average color as a string description.\n",
    "    caption = f\"\"\"The image is from {image_url}\n",
    "    It is about: {description}\n",
    "    The average color of the image is RGB:\n",
    "        ({avg_color[0]}, {avg_color[1]}, {avg_color[2]})\"\"\"\n",
    "\n",
    "    print(caption)  # For illustration purpose\n",
    "\n",
    "    return caption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cbafea62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mUser\u001b[0m (to Regular LLM Agent with Custom Func and LMM):\n",
      "\n",
      "Write a poet for my image:\n",
      "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "image_url https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0\n",
      "image_data <PIL.Image.Image image mode=RGB size=1920x1080 at 0x7F599DA4CCA0>\n",
      "lmm_client <autogen.oai.client.OpenAIWrapper object at 0x7f599da3ab20>\n",
      "The image is from https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0\n",
      "    It is about: Cute brown curly-haired puppy with blue collar indoors.\n",
      "    The average color of the image is RGB: \n",
      "        (170, 155, 137)\n",
      "\u001b[33mRegular LLM Agent with Custom Func and LMM\u001b[0m (to User):\n",
      "\n",
      "Beneath a sky of homely hue,\n",
      "Where RGB blends a gentle stew,\n",
      "Lies a pup of curls and capers,\n",
      "Bound by blue, his neck in drapers.\n",
      "\n",
      "Soft in gaze, his eyes implore,\n",
      "Warming hearts down to the core,\n",
      "Coat of brown, with tangles sweet,\n",
      "Whispers of play in each petite feet.\n",
      "\n",
      "In a world quite vast and wide,\n",
      "Indoors he sits, with pride inside.\n",
      "A silent wish, a breath, a start,\n",
      "Curly companion, a work of art.\n",
      "\n",
      "Within the frame, he's captured still,\n",
      "Yet, through the lens, his charm does spill.\n",
      "A tiny heartbeat in the quiet room,\n",
      "His presence banishes all gloom.\n",
      "\n",
      "A puppy's joy, in sepia tone,\n",
      "With collar blue, he reigns alone.\n",
      "A picture's worth, this moment's glee,\n",
      "Cute curly friend, for all to see.\n",
      "\n",
      "This poem encapsulates the essence of the cute brown curly-haired puppy wearing a blue collar, blending an emotional portrayal with the aesthetic elements you provided.\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "agent_with_vision_and_func = AssistantAgent(\n",
    "    name=\"Regular LLM Agent with Custom Func and LMM\", llm_config=gpt4_llm_config\n",
    ")\n",
    "\n",
    "vision_capability_with_func = VisionCapability(\n",
    "    lmm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300},\n",
    "    custom_caption_func=my_description,\n",
    ")\n",
    "vision_capability_with_func.add_to_agent(agent_with_vision_and_func)\n",
    "\n",
    "user.send(message=message, recipient=agent_with_vision_and_func, request_reply=True)"
   ]
  }
 ],
 "metadata": {
  "front_matter": {
   "description": "In AutoGen, leveraging multimodal models can be done through two different methodologies: MultimodalConversableAgent and VisionCapability.",
   "tags": [
    "multimodal",
    "gpt-4v"
   ]
  },
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